Compare matrices using PCA similarity factor
Compare matrices using PCA similarity factor
PCAsimilarity(cov.x, cov.y, ...) ## Default S3 method: PCAsimilarity(cov.x, cov.y, ret.dim = NULL, ...) ## S3 method for class 'list' PCAsimilarity(cov.x, cov.y = NULL, ..., repeat.vector = NULL, parallel = FALSE) ## S3 method for class 'mcmc_sample' PCAsimilarity(cov.x, cov.y, ..., parallel = FALSE)
cov.x |
Single covariance matrix ou list of covariance matrices. If cov.x is a single matrix, it is compared to cov.y. If cov.x is a list and no cov.y is suplied, all matrices are compared to each other. If cov.x is a list and cov.y is suplied, all matrices in cov.x are compared to cov.y. |
cov.y |
First argument is compared to cov.y. |
... |
aditional arguments passed to other methods |
ret.dim |
number of retained dimensions in the comparison. Defaults to all. |
repeat.vector |
Vector of repeatabilities for correlation correction. |
parallel |
if TRUE computations are done in parallel. Some foreach backend must be registered, like doParallel or doMC. |
Ratio of projected variance to total variance
Edgar Zanella Alvarenga
Singhal, A. and Seborg, D. E. (2005), Clustering multivariate time-series data. J. Chemometrics, 19: 427-438. doi: 10.1002/cem.945
c1 <- RandomMatrix(10) c2 <- RandomMatrix(10) PCAsimilarity(c1, c2) m.list <- RandomMatrix(10, 3) PCAsimilarity(m.list) PCAsimilarity(m.list, c1)
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